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ORCIDMao Ye 0006
Person information
- affiliation: University of Texas at Austin, TX, USA
Other persons with the same name
- Mao Ye — disambiguation page
- Mao Ye 0001
![[0000-0003-4760-8702]](https://dblp.uni-trier.de./img/orcid-mark.12x12.png "0000-0003-4760-8702")
— University of Electronic Science and Technology of China, School of Computer Science and Engineering, Center for Robotics, MoE, Key Laboratory for NeuroInformation, Chengdu, China (and 1 more) - Mao Ye 0002 — Pennsylvania State University, Department of Computer Science and Engineering, University Park, PA, USA
- Mao Ye 0003 — Nanjing University of Science and Technology, Department of Transportation Engineering, China
- Mao Ye 0004 — National University of Defense Technology, Nanjing, China
- Mao Ye 0005 — University of Kentucky, Lexington, KY, USA
- Mao Ye 0007 — Tianjin University, School of Microelectronics, China
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2020 – today
- 2024
[i21]Mao Ye, Gregory P. Meyer, Zaiwei Zhang, Dennis Park, Siva Karthik Mustikovela, Yuning Chai, Eric M. Wolff:
VLMine: Long-Tail Data Mining with Vision Language Models. CoRR abs/2409.15486 (2024)- [i20]Bo Liu, Mao Ye, Peter Stone, Qiang Liu:
Fine-Grained Gradient Restriction: A Simple Approach for Mitigating Catastrophic Forgetting. CoRR abs/2410.00868 (2024) - 2023
- [c18]Mao Ye, Gregory P. Meyer, Yuning Chai, Qiang Liu:
Efficient Transformer-based 3D Object Detection with Dynamic Token Halting. ICCV 2023: 8404-8416 - [c17]Xingchao Liu, Lemeng Wu, Mao Ye, Qiang Liu:
Learning Diffusion Bridges on Constrained Domains. ICLR 2023 - [i19]Mao Ye, Gregory P. Meyer, Yuning Chai, Qiang Liu:
Efficient Transformer-based 3D Object Detection with Dynamic Token Halting. CoRR abs/2303.05078 (2023) - 2022
- [c16]Mao Ye, Qiang Liu:
Centroid Approximation for Bootstrap: Improving Particle Quality at Inference. ICML 2022: 25469-25489 - [c15]Bo Liu, Mao Ye, Stephen Wright, Peter Stone, Qiang Liu:
BOME! Bilevel Optimization Made Easy: A Simple First-Order Approach. NeurIPS 2022 - [c14]Lemeng Wu, Chengyue Gong, Xingchao Liu, Mao Ye, Qiang Liu:
Diffusion-based Molecule Generation with Informative Prior Bridges. NeurIPS 2022 - [c13]Mao Ye, Lemeng Wu, Qiang Liu:
First Hitting Diffusion Models for Generating Manifold, Graph and Categorical Data. NeurIPS 2022 - [c12]Mao Ye, Qiang Liu:
Pareto navigation gradient descent: a first-order algorithm for optimization in pareto set. UAI 2022: 2246-2255 - [c11]Mao Ye, Ruichen Jiang, Haoxiang Wang, Dhruv Choudhary, Xiaocong Du, Bhargav Bhushanam, Aryan Mokhtari, Arun Kejariwal, Qiang Liu:
Future gradient descent for adapting the temporal shifting data distribution in online recommendation systems. UAI 2022: 2256-2266 - [i18]Xingchao Liu, Lemeng Wu, Mao Ye, Qiang Liu:
Let us Build Bridges: Understanding and Extending Diffusion Generative Models. CoRR abs/2208.14699 (2022) - [i17]Lemeng Wu, Chengyue Gong, Xingchao Liu, Mao Ye, Qiang Liu:
Diffusion-based Molecule Generation with Informative Prior Bridges. CoRR abs/2209.00865 (2022) - [i16]Mao Ye, Ruichen Jiang, Haoxiang Wang, Dhruv Choudhary, Xiaocong Du, Bhargav Bhushanam, Aryan Mokhtari, Arun Kejariwal, Qiang Liu:
Future Gradient Descent for Adapting the Temporal Shifting Data Distribution in Online Recommendation Systems. CoRR abs/2209.01143 (2022) - [i15]Mao Ye, Lemeng Wu, Qiang Liu:
First Hitting Diffusion Models. CoRR abs/2209.01170 (2022) - [i14]Mao Ye, Bo Liu, Stephen Wright, Peter Stone, Qiang Liu:
BOME! Bilevel Optimization Made Easy: A Simple First-Order Approach. CoRR abs/2209.08709 (2022) - 2021
- [c10]Xingchao Liu, Mao Ye, Dengyong Zhou, Qiang Liu:
Post-training Quantization with Multiple Points: Mixed Precision without Mixed Precision. AAAI 2021: 8697-8705 - [c9]Chengyue Gong, Tongzheng Ren, Mao Ye, Qiang Liu:
MaxUp: Lightweight Adversarial Training With Data Augmentation Improves Neural Network Training. CVPR 2021: 2474-2483 - [c8]Lizhen Nie, Mao Ye, Qiang Liu, Dan Nicolae:
VCNet and Functional Targeted Regularization For Learning Causal Effects of Continuous Treatments. ICLR 2021 - [c7]Chengyue Gong, Mao Ye, Qiang Liu:
argmax centroid. NeurIPS 2021: 7012-7024 - [i13]Lizhen Nie, Mao Ye, Qiang Liu, Dan Nicolae:
VCNet and Functional Targeted Regularization For Learning Causal Effects of Continuous Treatments. CoRR abs/2103.07861 (2021) - [i12]Mao Ye, Qiang Liu:
Pareto Navigation Gradient Descent: a First-Order Algorithm for Optimization in Pareto Set. CoRR abs/2110.08713 (2021) - [i11]Mao Ye, Qiang Liu:
Centroid Approximation for Bootstrap. CoRR abs/2110.08720 (2021) - 2020
- [c6]Mao Ye, Chengyue Gong, Qiang Liu:
SAFER: A Structure-free Approach for Certified Robustness to Adversarial Word Substitutions. ACL 2020: 3465-3475 - [c5]Mao Ye, Chengyue Gong, Lizhen Nie, Denny Zhou, Adam R. Klivans, Qiang Liu:
Good Subnetworks Provably Exist: Pruning via Greedy Forward Selection. ICML 2020: 10820-10830 - [c4]Denny Zhou, Mao Ye, Chen Chen, Tianjian Meng, Mingxing Tan, Xiaodan Song, Quoc V. Le, Qiang Liu, Dale Schuurmans:
Go Wide, Then Narrow: Efficient Training of Deep Thin Networks. ICML 2020: 11546-11555 - [c3]Mao Ye, Tongzheng Ren, Qiang Liu:
Stein Self-Repulsive Dynamics: Benefits From Past Samples. NeurIPS 2020 - [c2]Mao Ye, Lemeng Wu, Qiang Liu:
Greedy Optimization Provably Wins the Lottery: Logarithmic Number of Winning Tickets is Enough. NeurIPS 2020 - [c1]Dinghuai Zhang, Mao Ye, Chengyue Gong, Zhanxing Zhu, Qiang Liu:
Black-Box Certification with Randomized Smoothing: A Functional Optimization Based Framework. NeurIPS 2020 - [i10]ChengYue Gong, Tongzheng Ren, Mao Ye, Qiang Liu:
MaxUp: A Simple Way to Improve Generalization of Neural Network Training. CoRR abs/2002.09024 (2020) - [i9]Xingchao Liu, Mao Ye, Dengyong Zhou, Qiang Liu:
Post-training Quantization with Multiple Points: Mixed Precision without Mixed Precision. CoRR abs/2002.09049 (2020) - [i8]Mao Ye, Tongzheng Ren, Qiang Liu:
Stein Self-Repulsive Dynamics: Benefits From Past Samples. CoRR abs/2002.09070 (2020) - [i7]Dinghuai Zhang, Mao Ye, Chengyue Gong, Zhanxing Zhu, Qiang Liu:
Black-Box Certification with Randomized Smoothing: A Functional Optimization Based Framework. CoRR abs/2002.09169 (2020) - [i6]Mao Ye, Chengyue Gong, Lizhen Nie, Denny Zhou, Adam R. Klivans, Qiang Liu:
Good Subnetworks Provably Exist: Pruning via Greedy Forward Selection. CoRR abs/2003.01794 (2020) - [i5]Lemeng Wu, Mao Ye, Qi Lei, Jason D. Lee, Qiang Liu:
Steepest Descent Neural Architecture Optimization: Escaping Local Optimum with Signed Neural Splitting. CoRR abs/2003.10392 (2020) - [i4]Mao Ye, Chengyue Gong, Qiang Liu:
SAFER: A Structure-free Approach for Certified Robustness to Adversarial Word Substitutions. CoRR abs/2005.14424 (2020) - [i3]Denny Zhou, Mao Ye, Chen Chen, Tianjian Meng, Mingxing Tan, Xiaodan Song, Quoc V. Le, Qiang Liu, Dale Schuurmans:
Go Wide, Then Narrow: Efficient Training of Deep Thin Networks. CoRR abs/2007.00811 (2020) - [i2]Mao Ye, Dhruv Choudhary, Jiecao Yu, Ellie Wen, Zeliang Chen, Jiyan Yang, Jongsoo Park, Qiang Liu, Arun Kejariwal:
Adaptive Dense-to-Sparse Paradigm for Pruning Online Recommendation System with Non-Stationary Data. CoRR abs/2010.08655 (2020) - [i1]Mao Ye, Lemeng Wu, Qiang Liu:
Greedy Optimization Provably Wins the Lottery: Logarithmic Number of Winning Tickets is Enough. CoRR abs/2010.15969 (2020)
Coauthor Index
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